An Ultra-Area-Efficient 1024-Point In-Memory FFT Processor

Abstract

Current computation architectures rely on more processor-centric design principles. On the other hand, the inevitable increase in the amount of data that applications need forces researchers to design novel processor architectures that are more data-centric. By following this principle, this study proposes an area-efficient Fast Fourier Transform (FFT) processor through in-memory computing. The proposed architecture occupies the smallest footprint of around 0.1 ${\mathrm{mm}}^2$ inside its class together with acceptable power efficiency. According to the results, the processor exhibits the highest area efficiency ( $\mathrm{FFT}/\mathrm{s}/\mathrm{area}$ ) among the existing FFT processors in the current literature.